Novel Adaptive Generalized Eigenvector Estimation Algorithm and Its Characteristic Analysis

XU Zhongying; GAO Yingbin; KONG Xiangyu

doi:10.11999/JEIT200477

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 254-260

XU Zhongying, GAO Yingbin, KONG Xiangyu. Novel Adaptive Generalized Eigenvector Estimation Algorithm and Its Characteristic Analysis[J]. Journal of Electronics & Information Technology, 2022, 44(1): 254-260. doi: 10.11999/JEIT200477

Citation:

XU Zhongying, GAO Yingbin, KONG Xiangyu. Novel Adaptive Generalized Eigenvector Estimation Algorithm and Its Characteristic Analysis[J]. Journal of Electronics & Information Technology, 2022, 44(1): 254-260. doi: 10.11999/JEIT200477

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Novel Adaptive Generalized Eigenvector Estimation Algorithm and Its Characteristic Analysis

doi: 10.11999/JEIT200477

1.
College of Missile Engineering, The Rocket Force University of Engineering, Xi’an 710025, China
2.
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China

Funds: The National Natural Science Foundation of China (62106242, 62101579, 61903375), The Natural Science Foundation of Shaanxi Province (2020JM-356)

Received Date: 2020-06-12
Rev Recd Date: 2021-09-01

Available Online: 2021-11-02

Publish Date: 2022-01-10

Abstract

Abstract

In order to develop fast and stable algorithm for estimating generalized eigenvector, a novel neuron-based algorithm is proposed for extracting the single generalized eigenvector. Through analyzing all of the stationary points, it is proved that the single estimation algorithm reaches the steady state if and only if the weight vector of the neural network is equal to the generalized eigenvector corresponding to the smallest generalized eigenvalue of a matrix pencil. The dynamic analysis of the single estimation algorithm is accomplished by the deterministic discrete time method and some boundary conditions are also obtained to guarantee the algorithm’s convergence. Trough applying the inflation technique, the single generalized eigenvector estimation algorithm is extended into a multiple generalized eigenvector estimation algorithm, and the number of the generalized eigenvectors can be increased according to actual requirement. Simulation experiments results prove that the proposed algorithm has good convergence, and the convergence speed is better than some existing algorithms.
- Generalized eigenvector,
- Stability analysis,
- Dynamic analysis,
- Multiple extraction

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References(19)

References

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